Development of Platoon-Based Intersection Control Strategies (PBICS) for Fully Connected Automated Vehicles and Their Challenges

This dissertation aims at developing Platoon-Based Intersection Control Strategies (PBICS) and evaluating their challenges with computation and communication.
Firstly, PBICS at a single intersection for fully connected automated vehicles were studied. All vehicles in the system are Connected and Autonomous Vehicles (CAVs) and capable of wireless Vehicle-to-Intersection (V2I) and Vehicle-to-Vehicle (V2V) communications. The idea of the study is to implement vehicle platoons (e.g., consecutive vehicles in a row) to the intersection control problem. The core of PBICS is to decide optimal platoon size (i.e., number of vehicles in the platoon); because the efficiency of intersection relies on vehicle headway and arrival distribution.
There are two strategies proposed in this dissertation, which provide solutions in both heuristic and optimized approaches. In the heuristic approach (hPBICS), Intersection Occupancy Index (I.O.I.) was developed to determine platoon size. In the optimized approach (oPBICS), multiple platoons were considered in the optimization problem resulting in optimal trajectories with optimal platoon sizes. Those solutions were compared to three baseline algorithms (i.e., MICA, pre-sized platoons, and AIM∗), and the results indicated that PBICS strategies improve mobility and fuel economy.
Secondly, the benefit of vehicle platooning with coordination of intersections was explored. Especially on major/minor roads, vehicle platooning is expected to have more benefits on travel time reduction with forming more frequent and longer platoons on major roads. The study area was expanded from an isolated intersection to two consecutive intersections, and the optimizations of hPBICS and oPBICS were simulated in the same manner converting throughput maximization (space-domain) to travel time minimization (time-domain).
A comparison between coordinated control (i.e., solving two intersection traffic in one optimization problem) and separated control (i.e., two separate optimizations for each intersection) was conducted to see the coordination effect. As a result, the impact of intersection coordination and vehicle platooning significantly improved intersection performance, especially on major/minor roads.
Lastly, the strategies were further evaluated with Vehicle-to-Everything communication as a sensitivity analysis. Key factors of computation and communication were considered in the analysis, such as computation time, communication type, signal power, line of sight, and additional constant delay compensating for unknown surrounding signals and security encryption and decryption.
The sensitivity results indicated that the current computing capability could only manage hPBICS in real-time due to computational complexity. Among the factors considered, traffic volume and line of sight was revealed to play an important role to avoid the system failure of intersection control.
The vehicle platooning strategies enable intersection crossing to be more efficient. Despite there is an obvious trade-off between travel time and fuel consumption, travel time-minimizing control strategies are necessary where the traffic is heavy (e.g., peak hours). The effectiveness of vehicle platooning can make synergy with intersection coordination, especially when the directional traffic flows are unequal. However, computation and communication are still the challenges to deploy the strategies in the real world. Thus, this dissertation suggests hPBICS for fully CAVs field implementation according to the simulation results.